Generative AI
Generative AI is a broad term for any type of AI system capable of creating new forms of humanlike creative content — such as text, images, music, audio, video and more — using generative models.
Generative models are algorithms that learn the various patterns and structures of input training data before generating new outputs with similar characteristics.
The massive popularity of ChatGPT — perhaps the most successful proof of concept in the history of technology — has pushed generative AI front and center among business and IT leaders.
See what's trending
How WWT is Harnessing Generative AI to Drive Internal Business Value
11 Policy Considerations for Implementing Responsible AI
The Only Way to Succeed with AI is a Full Stack Approach
Practical AI: The Ideal Approach to Generative AI
The 3 Pillars of Practical AI
How does generative AI work?
Generative AI models rely on various neural network architectures (e.g., transformers, generative adversarial networks (GANs), variational auto-encoders (VAEs) and diffusion networks) to produce their humanlike outputs. Each works a bit differently.
Transformers
Transformers use mechanisms called attention modules to efficiently process input data in parallel, learn their relationships, and then generate new forms of creative content. Examples include GPT-3 and DALL-E.
Variational auto-encoders (VAEs)
VAEs leverage encoders to transform input data into latent vectors, while decoders learn to reconstruct samples of these vectors into original outputs that closely resemble the input data. Examples include VAE-GAN and MusicVAE.
Generative adversarial networks (GANs)
GANs leverage adversarial learning — a technique to simultaneously train two neural networks against each other in a competitive game-like scenario — to improve the accuracy of both models over time. Examples include StyleGAN and CycleGAN.
Diffusion networks
A type of deep neural network that adds noise to a training dataset and then reverses the process to recover the data, gradually learning to remove the noise and produce realistic data similar to the original input. Examples include Stable Diffusion and Midjourney.
Responsible AI
Generative AI is an exciting and rapidly evolving field with many benefits and opportunities for innovation. Yet there are also challenges and risks, including issues touching on data quality and privacy, ethics, bias and fairness, and the potential for misuse and abuse by bad actors.
WWT is committed to Responsible AI — the practice of designing, developing and deploying AI systems in a way that is safe, ethical and fair. By considering the downstream risks and benefits of AI systems from a holistic perspective, organizations can often mitigate prospective harm.
At WWT, we've been helping our clients and partners implement responsible business and technology solutions related to AI for years. Our practical approach helps organizations leverage the latest AI systems to achieve results while maturing their data strategy and high-performance architecture (HPA) capabilities.
Connect with our experts
What's popular
Exploring Different Types and Sizes of LLMs — the Engines that Power Generative AI | Experts
Practical AI: The Ideal Approach to Generative AI
Generative AI: Risks, Rewards and a Framework for Utilization
Embracing the Benefits of Generative AI in Cybersecurity Operations
How Generative AI Impacts Identity and Access Management
What's new
How WWT is Developing and Driving Generative AI Initiatives | Experts
AI to Cyber... Fuel to Fire
Partner POV | VMware Private AI: Democratize generative AI and ignite innovation for all enterprises
Partner POV | The Rise of Private AI
FedTalks 2023 Recap: Securing Government Operations and AI's Role in Citizen Experience
Preparing for Ransomware in the Age of Generative AI
Cohesity Uses Generative AI to Help You Extract More Value from Your Backup Data
WWT CEO: Why Partnering With IT Startups Drives 'A Lot Of Value' For Customers
Generative AI Briefing
Partner POV | Eaton is the 'cool' choice as power needs heat up for generative AI systems
Partner POV | Achieving a sustainable future for AI
An Introduction to AI Model Security
AI Security Briefing
Drive Customer Stickiness and Enhance the Bottom Line: The Power of LLMs and Generative AI in QSRs
The OWASP LLM Top 10 List: A Practical Guide for CISOs
Google Cloud Next and the Age of Generative AI
Dive in to ChatGPT and Large Language Models | Briefing
Unlocking Generative AI for Civilian Agencies | Event Recap
Exploring Different Types and Sizes of LLMs — the Engines that Power Generative AI | Experts
Harnessing the Power of Generative AI: Why Cloud Cost Optimization is Key